Paper Info
Title
Deep neural network features for horses identity recognition using multiview horses' face pattern.
Abstract
To control the state of horses in the born, breeders needs a monitoring system with a surveillance camera that can identify and distinguish between horses. We proposed in [5] a method of horse's identification at a distance using the frontal facial biometric modality. Due to the change of views, the face recognition becomes more difficult. In this paper, the number of images used in our THoDBRL' 2015 database (Tunisian Horses DataBase of Regim Lab) is augmented by adding other images of other views. Thus, we used front, right and left profile face's view. Moreover, we suggested an approach for multiview face recognition. First, we proposed to use the Gabor filter for face characterization. Next, due to the augmentation of the number of images, and the large number of Gabor features, we proposed to test the Deep Neural Network with the auto-encoder to obtain the more pertinent features and to reduce the size of features vector. Finally, we performed the proposed approach on our THoDBRL' 2015 database and we used the linear SVM for classification.
Year
DOI
Venue
2016
10.1117/12.2269064
Proceedings of SPIE
Keywords
Field
DocType
Multiviews faces,horses' face identification,Gabor features,deep neural network,Auto-encoder
Facial recognition system,Computer vision,Three-dimensional face recognition,Monitoring system,Computer science,Gabor filter,Artificial intelligence,Biometrics,Artificial neural network,Identity recognition,Linear svm
Conference
Volume
ISSN
Citations 
10341
0277-786X
1
PageRank 
References 
Authors
0.35
0
3
Name
Order
Citations
PageRank
Islem Jarraya131.05
Wael Ouarda2347.36
Mohamed Adel Alimi31947217.16